Although these methods can be competent to effectively handle attribute-incomplete problem, they fail to perform preferably on datasets with missing attributes, i.e., the entire attributes of specific nodes are missing.
More recently, an advanced algorithm SAT is proposed to learn on attribute-missing graphs. It adopts two decoupled sub-networks to process node attributes and graph structures, and then utilize structure information to conduct data completion under the guidance of a shared-latent space assumption [1]}. Despite its success, SAT not only isolates the learning processes of attribute and structure embeddings but also heavily relies on a prior distribution assumption for latent representation learning. Thus it fails to take full advantages of both types information, leading to less discriminative latent representations. In contrast, our method allows both learned features to sufficiently interact with each other via a structure-attribute mutual enhanced learning strategy. As a results, the learned latent embedding has the potential to be more discriminative for data completion.
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